
lr = 5e-6  # learning rate
accum_grid = 4
batch_size = 2
epochs = 10
max_length = 4096
# n_model = 21128
n_model = 768
model_name_or_path = 'bigbird'
# model_name_or_path = '/media/data/zc/bert_attention/longformer'
checkpoint = 'bigbird_checkpoint/grid_new/2-newdata-weight4-4096/'
train_path = 'dataset/grid_new/train_dataset_2_newdata.csv'
valid_path = 'dataset/grid_new/valid_dataset_2_newdata.csv'
test_path = 'dataset/许昌历史通话文本合并通话记录信息_20230801-20231222_to_pred.xlsx'
# train_path = 'public_dataset/train_dataset.txt'
# valid_path = 'public_dataset/valid_dataset.txt'
# cls = {'C3-Art': 0, 'C4-Literature': 1, 'C5-Education': 2, 'C6-Philosophy': 3, 'C7-History': 4, 'C11-Space': 5,
#        'C15-Energy': 6, 'C16-Electronics': 7, 'C17-Communication': 8, 'C19-Computer': 9, 'C23-Mine': 10,
#        'C29-Transport': 11, 'C31-Enviornment': 12, 'C32-Agriculture': 13, 'C34-Economy': 14, 'C35-Law': 15,
#        'C36-Medical': 16, 'C37-Military': 17, 'C38-Politics': 18, 'C39-Sports': 19}

# cls = {'6-内部通话': 0, '1-红色': 1, '5-绿色': 2}
# cls = {'inner': 0, 'yes': 1,  'no': 2}
cls = {'no': 0, 'yes': 1}
# cls = {'Mine': 0, 'Art': 1, 'Computer': 2, 'Military': 3, 'Electronics': 4, 'Transport': 5, 'History': 6, 'Agriculture': 7, 'Energy': 8, 'Space': 9, 'Literature': 10, 'Enviornment': 11, 'Medical': 12, 'Economy': 13, 'Sports': 14, 'Education': 15, 'Politics': 16, 'Law': 17, 'Philosophy': 18, 'Communication': 19}
